ⓘ Data stream mining

                                     

ⓘ Data stream mining

Data Stream Mining is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities.

In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i.e. the goal of the prediction, the class to be predicted or the target value to be predicted, may change over time. This problem is referred to as concept drift.

Examples of data streams include computer network traffic, phone conversations, ATM transactions, web searches, and sensor data. Data stream mining can be considered a subfield of data mining, machine learning, and knowledge discovery.

                                     

1. Software for data stream mining

  • RapidMiner: commercial software for knowledge discovery, data mining, and machine learning also featuring data stream mining, learning time-varying concepts, and tracking drifting concept if used in combination with its data stream mining plugin formerly: Concept Drift plugin)
  • MOA Massive Online Analysis: free open-source software specific for mining data streams with concept drift. It has several machine learning algorithms. Also it contains a prequential evaluation method, the EDDM concept drift methods, a reader of ARFF real datasets, and artificial stream generators as SEA concepts, STAGGER, rotating hyperplane, random tree, and random radius based functions. MOA supports bi-directional interaction with Weka machine learning.
                                     

2. Events

  • IEEE International Workshop on Mining Evolving and Streaming Data IWMESD 2006 to be held in conjunction with the 2006 IEEE International Conference on Data Mining ICDM-2006 in Hong Kong in December 2006.
  • International Workshop on Knowledge Discovery from Ubiquitous Data Streams held in conjunction with the 18th European Conference on Machine Learning ECML and the 11th European Conference on Principles and Practice of Knowledge Discovery in Databases PKDD in Warsaw, Poland, in September 2007.
  • Fourth International Workshop on Knowledge Discovery from Data Streams IWKDDS to be held in conjunction with the 17th European Conference on Machine Learning ECML and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases PKDD ECML/PKDD-2006 in Berlin, Germany, in September 2006.
  • ACM Symposium on Applied Computing Data Streams Track held in conjunction with the 2007 ACM Symposium on Applied Computing SAC-2007 in Seoul, Korea, in March 2007.
  • International Workshop on Ubiquitous Data Mining held in conjunction with the International Joint Conference on Artificial Intelligence IJCAI in Beijing, China, August 3–5, 2013.
                                     

3. Books

  • Lughofer, Edwin 2011. Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications. Studies in Fuzziness and Soft Computing. 266. Heidelberg: Springer. p. 456. doi:10.1007/978-3-642-18087-3. ISBN 9783642180866.
  • Bifet, Albert; Gavaldà, Ricard; Holmes, Geoff; Pfahringer, Bernhard 2018. Machine Learning for Data Streams with Practical Examples in MOA. Adaptive Computation and Machine Learning. MIT Press. p. 288. ISBN 9780262037792.
  • Gama, João; Gaber, Mohamed Medhat, eds. 2007. Learning from Data Streams: Processing Techniques in Sensor Networks. Springer. p. 244. doi:10.1007/3-540-73679-4. ISBN 9783540736783.
  • Sayed-Mouchaweh, Moamar; Lughofer, Edwin, eds. 2012. Learning in Non-Stationary Environments: Methods and Applications. New York: Springer. p. 440. CiteSeerX 10.1.1.709.437. doi:10.1007/978-1-4419-8020-5. ISBN 9781441980199.
  • Ganguly, Auroop R.; Gama, João; Omitaomu, Olufemi A.; Gaber, Mohamed M.; Vatsavai, Ranga R., eds. 2008. Knowledge Discovery from Sensor Data. Industrial Innovation. CRC Press. p. 215. ISBN 9781420082326.
  • Gama, João 2010. Knowledge Discovery from Data Streams. Data Mining and Knowledge Discovery. Chapman and Hall. p. 255. ISBN 9781439826119.